In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
netflix = pd.read_csv('netflix_titles.csv')
netflix.head()
Out[2]:
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 81145628 | Movie | Norm of the North: King Sized Adventure | Richard Finn, Tim Maltby | Alan Marriott, Andrew Toth, Brian Dobson, Cole... | United States, India, South Korea, China | September 9, 2019 | 2019 | TV-PG | 90 min | Children & Family Movies, Comedies | Before planning an awesome wedding for his gra... |
| 1 | 80117401 | Movie | Jandino: Whatever it Takes | NaN | Jandino Asporaat | United Kingdom | September 9, 2016 | 2016 | TV-MA | 94 min | Stand-Up Comedy | Jandino Asporaat riffs on the challenges of ra... |
| 2 | 70234439 | TV Show | Transformers Prime | NaN | Peter Cullen, Sumalee Montano, Frank Welker, J... | United States | September 8, 2018 | 2013 | TV-Y7-FV | 1 Season | Kids' TV | With the help of three human allies, the Autob... |
| 3 | 80058654 | TV Show | Transformers: Robots in Disguise | NaN | Will Friedle, Darren Criss, Constance Zimmer, ... | United States | September 8, 2018 | 2016 | TV-Y7 | 1 Season | Kids' TV | When a prison ship crash unleashes hundreds of... |
| 4 | 80125979 | Movie | #realityhigh | Fernando Lebrija | Nesta Cooper, Kate Walsh, John Michael Higgins... | United States | September 8, 2017 | 2017 | TV-14 | 99 min | Comedies | When nerdy high schooler Dani finally attracts... |
In [3]:
netflix_shows = netflix[netflix['type']=='TV Show']
netflix_movies = netflix[netflix['type']=='Movie']
In [4]:
netflix_movies
Out[4]:
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 81145628 | Movie | Norm of the North: King Sized Adventure | Richard Finn, Tim Maltby | Alan Marriott, Andrew Toth, Brian Dobson, Cole... | United States, India, South Korea, China | September 9, 2019 | 2019 | TV-PG | 90 min | Children & Family Movies, Comedies | Before planning an awesome wedding for his gra... |
| 1 | 80117401 | Movie | Jandino: Whatever it Takes | NaN | Jandino Asporaat | United Kingdom | September 9, 2016 | 2016 | TV-MA | 94 min | Stand-Up Comedy | Jandino Asporaat riffs on the challenges of ra... |
| 4 | 80125979 | Movie | #realityhigh | Fernando Lebrija | Nesta Cooper, Kate Walsh, John Michael Higgins... | United States | September 8, 2017 | 2017 | TV-14 | 99 min | Comedies | When nerdy high schooler Dani finally attracts... |
| 6 | 70304989 | Movie | Automata | Gabe Ibáñez | Antonio Banderas, Dylan McDermott, Melanie Gri... | Bulgaria, United States, Spain, Canada | September 8, 2017 | 2014 | R | 110 min | International Movies, Sci-Fi & Fantasy, Thrillers | In a dystopian future, an insurance adjuster f... |
| 7 | 80164077 | Movie | Fabrizio Copano: Solo pienso en mi | Rodrigo Toro, Francisco Schultz | Fabrizio Copano | Chile | September 8, 2017 | 2017 | TV-MA | 60 min | Stand-Up Comedy | Fabrizio Copano takes audience participation t... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5577 | 80085438 | Movie | Frank and Cindy | G.J. Echternkamp | NaN | United States | April 1, 2016 | 2007 | TV-MA | 70 min | Documentaries | Frank was a rising pop star when he married Ci... |
| 5578 | 80085439 | Movie | Frank and Cindy | G.J. Echternkamp | Rene Russo, Oliver Platt, Johnny Simmons, Jane... | United States | April 1, 2016 | 2015 | R | 102 min | Comedies, Dramas, Independent Movies | A student filmmaker vengefully turns his camer... |
| 5579 | 80011846 | Movie | Iverson | Zatella Beatty | Allen Iverson | United States | April 1, 2016 | 2014 | NR | 88 min | Documentaries, Sports Movies | This unfiltered documentary follows the rocky ... |
| 5580 | 80064521 | Movie | Jeremy Scott: The People's Designer | Vlad Yudin | Jeremy Scott | United States | April 1, 2016 | 2015 | PG-13 | 109 min | Documentaries | The journey of fashion designer Jeremy Scott f... |
| 6231 | 80116008 | Movie | Little Baby Bum: Nursery Rhyme Friends | NaN | NaN | NaN | NaN | 2016 | NaN | 60 min | Movies | Nursery rhymes and original music for children... |
4265 rows × 12 columns
In [5]:
netflix_shows
Out[5]:
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 70234439 | TV Show | Transformers Prime | NaN | Peter Cullen, Sumalee Montano, Frank Welker, J... | United States | September 8, 2018 | 2013 | TV-Y7-FV | 1 Season | Kids' TV | With the help of three human allies, the Autob... |
| 3 | 80058654 | TV Show | Transformers: Robots in Disguise | NaN | Will Friedle, Darren Criss, Constance Zimmer, ... | United States | September 8, 2018 | 2016 | TV-Y7 | 1 Season | Kids' TV | When a prison ship crash unleashes hundreds of... |
| 5 | 80163890 | TV Show | Apaches | NaN | Alberto Ammann, Eloy AzorÃn, Verónica Echegui,... | Spain | September 8, 2017 | 2016 | TV-MA | 1 Season | Crime TV Shows, International TV Shows, Spanis... | A young journalist is forced into a life of cr... |
| 8 | 80117902 | TV Show | Fire Chasers | NaN | NaN | United States | September 8, 2017 | 2017 | TV-MA | 1 Season | Docuseries, Science & Nature TV | As California's 2016 fire season rages, brave ... |
| 26 | 80244601 | TV Show | Castle of Stars | NaN | Chaiyapol Pupart, Jintanutda Lummakanon, Worra... | NaN | September 7, 2018 | 2015 | TV-14 | 1 Season | International TV Shows, Romantic TV Shows, TV ... | As four couples with different lifestyles go t... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6228 | 80159925 | TV Show | Kikoriki | NaN | Igor Dmitriev | NaN | NaN | 2010 | TV-Y | 2 Seasons | Kids' TV | A wacky rabbit and his gang of animal pals hav... |
| 6229 | 80000063 | TV Show | Red vs. Blue | NaN | Burnie Burns, Jason Saldaña, Gustavo Sorola, G... | United States | NaN | 2015 | NR | 13 Seasons | TV Action & Adventure, TV Comedies, TV Sci-Fi ... | This parody of first-person shooter games, mil... |
| 6230 | 70286564 | TV Show | Maron | NaN | Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh... | United States | NaN | 2016 | TV-MA | 4 Seasons | TV Comedies | Marc Maron stars as Marc Maron, who interviews... |
| 6232 | 70281022 | TV Show | A Young Doctor's Notebook and Other Stories | NaN | Daniel Radcliffe, Jon Hamm, Adam Godley, Chris... | United Kingdom | NaN | 2013 | TV-MA | 2 Seasons | British TV Shows, TV Comedies, TV Dramas | Set during the Russian Revolution, this comic ... |
| 6233 | 70153404 | TV Show | Friends | NaN | Jennifer Aniston, Courteney Cox, Lisa Kudrow, ... | United States | NaN | 2003 | TV-14 | 10 Seasons | Classic & Cult TV, TV Comedies | This hit sitcom follows the merry misadventure... |
1969 rows × 12 columns
In [6]:
sns.set_style('whitegrid')
sns.countplot(x='type',data=netflix)
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb439e608>
In [7]:
netflix_date = netflix_shows[['date_added']].dropna()
In [8]:
netflix_date
Out[8]:
| date_added | |
|---|---|
| 2 | September 8, 2018 |
| 3 | September 8, 2018 |
| 5 | September 8, 2017 |
| 8 | September 8, 2017 |
| 26 | September 7, 2018 |
| ... | ... |
| 6218 | April 10, 2019 |
| 6219 | April 1, 2019 |
| 6220 | April 1, 2016 |
| 6221 | April 1, 2016 |
| 6222 | April 1, 2014 |
1959 rows × 1 columns
In [10]:
netflix_date['year'] = netflix_date['date_added'].apply(lambda x : x.split(',')[-1])
In [11]:
netflix_date
Out[11]:
| date_added | year | |
|---|---|---|
| 2 | September 8, 2018 | 2018 |
| 3 | September 8, 2018 | 2018 |
| 5 | September 8, 2017 | 2017 |
| 8 | September 8, 2017 | 2017 |
| 26 | September 7, 2018 | 2018 |
| ... | ... | ... |
| 6218 | April 10, 2019 | 2019 |
| 6219 | April 1, 2019 | 2019 |
| 6220 | April 1, 2016 | 2016 |
| 6221 | April 1, 2016 | 2016 |
| 6222 | April 1, 2014 | 2014 |
1959 rows × 2 columns
In [12]:
netflix_date['month'] = netflix_date['date_added'].apply(lambda x : x.lstrip().split(' ')[0])
In [13]:
netflix_date
Out[13]:
| date_added | year | month | |
|---|---|---|---|
| 2 | September 8, 2018 | 2018 | September |
| 3 | September 8, 2018 | 2018 | September |
| 5 | September 8, 2017 | 2017 | September |
| 8 | September 8, 2017 | 2017 | September |
| 26 | September 7, 2018 | 2018 | September |
| ... | ... | ... | ... |
| 6218 | April 10, 2019 | 2019 | April |
| 6219 | April 1, 2019 | 2019 | April |
| 6220 | April 1, 2016 | 2016 | April |
| 6221 | April 1, 2016 | 2016 | April |
| 6222 | April 1, 2014 | 2014 | April |
1959 rows × 3 columns
In [14]:
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
In [15]:
netflix_date.groupby('year')['month'].value_counts()
Out[15]:
year month
2008 February 1
2012 August 1
July 1
October 1
2013 October 2
..
2019 February 59
April 58
December 53
January 44
2020 January 37
Name: month, Length: 72, dtype: int64
In [16]:
netflix_date.groupby('year')['month'].value_counts().unstack()
Out[16]:
| month | April | August | December | February | January | July | June | March | May | November | October | September |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| year | ||||||||||||
| 2008 | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2012 | NaN | 1.0 | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | 1.0 | NaN |
| 2013 | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 2.0 | 1.0 |
| 2014 | 1.0 | NaN | 1.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | 3.0 | NaN | NaN |
| 2015 | 6.0 | NaN | 7.0 | 1.0 | NaN | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 5.0 | 1.0 |
| 2016 | 8.0 | 19.0 | 44.0 | 7.0 | 29.0 | 12.0 | 8.0 | 4.0 | 5.0 | 18.0 | 19.0 | 19.0 |
| 2017 | 31.0 | 41.0 | 43.0 | 19.0 | 15.0 | 35.0 | 30.0 | 39.0 | 26.0 | 33.0 | 39.0 | 36.0 |
| 2018 | 34.0 | 42.0 | 75.0 | 27.0 | 27.0 | 32.0 | 31.0 | 40.0 | 32.0 | 48.0 | 55.0 | 49.0 |
| 2019 | 58.0 | 71.0 | 53.0 | 59.0 | 44.0 | 78.0 | 61.0 | 74.0 | 68.0 | 87.0 | 84.0 | 66.0 |
| 2020 | NaN | NaN | NaN | NaN | 37.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
In [17]:
netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)
Out[17]:
| month | April | August | December | February | January | July | June | March | May | November | October | September |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| year | ||||||||||||
| 2008 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2012 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 2013 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 2014 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 |
| 2015 | 6.0 | 0.0 | 7.0 | 1.0 | 0.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 5.0 | 1.0 |
| 2016 | 8.0 | 19.0 | 44.0 | 7.0 | 29.0 | 12.0 | 8.0 | 4.0 | 5.0 | 18.0 | 19.0 | 19.0 |
| 2017 | 31.0 | 41.0 | 43.0 | 19.0 | 15.0 | 35.0 | 30.0 | 39.0 | 26.0 | 33.0 | 39.0 | 36.0 |
| 2018 | 34.0 | 42.0 | 75.0 | 27.0 | 27.0 | 32.0 | 31.0 | 40.0 | 32.0 | 48.0 | 55.0 | 49.0 |
| 2019 | 58.0 | 71.0 | 53.0 | 59.0 | 44.0 | 78.0 | 61.0 | 74.0 | 68.0 | 87.0 | 84.0 | 66.0 |
| 2020 | 0.0 | 0.0 | 0.0 | 0.0 | 37.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
In [19]:
netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
Out[19]:
| year | 2008 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|
| month | ||||||||||
| December | 0.0 | 0.0 | 0.0 | 1.0 | 7.0 | 44.0 | 43.0 | 75.0 | 53.0 | 0.0 |
| November | 0.0 | 0.0 | 0.0 | 3.0 | 2.0 | 18.0 | 33.0 | 48.0 | 87.0 | 0.0 |
| October | 0.0 | 1.0 | 2.0 | 0.0 | 5.0 | 19.0 | 39.0 | 55.0 | 84.0 | 0.0 |
| September | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 19.0 | 36.0 | 49.0 | 66.0 | 0.0 |
| August | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 19.0 | 41.0 | 42.0 | 71.0 | 0.0 |
| July | 0.0 | 1.0 | 0.0 | 0.0 | 3.0 | 12.0 | 35.0 | 32.0 | 78.0 | 0.0 |
| June | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 8.0 | 30.0 | 31.0 | 61.0 | 0.0 |
| May | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 5.0 | 26.0 | 32.0 | 68.0 | 0.0 |
| April | 0.0 | 0.0 | 1.0 | 1.0 | 6.0 | 8.0 | 31.0 | 34.0 | 58.0 | 0.0 |
| March | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 | 4.0 | 39.0 | 40.0 | 74.0 | 0.0 |
| February | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 7.0 | 19.0 | 27.0 | 59.0 | 0.0 |
| January | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 29.0 | 15.0 | 27.0 | 44.0 | 37.0 |
In [20]:
df = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
In [23]:
plt.figure(figsize=(12,7))
plt.pcolor(df,cmap='viridis',edgecolors='black',lw=2)
Out[23]:
<matplotlib.collections.PolyCollection at 0x20fb45c9188>
In [32]:
plt.figure(figsize=(20,20))
sns.heatmap(df,cmap='coolwarm',linecolor='white',linewidths=2)
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb496bf48>
In [34]:
plt.figure(figsize=(12,7))
sns.countplot(x='rating',data=netflix_movies)
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fb59a6f08>
In [35]:
imdb = pd.read_csv('IMDB-Movie-Data.csv')
imdb.head()
Out[35]:
| Rank | Title | Genre | Description | Director | Actors | Year | Runtime (Minutes) | Rating | Votes | Revenue (Millions) | Metascore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Guardians of the Galaxy | Action,Adventure,Sci-Fi | A group of intergalactic criminals are forced ... | James Gunn | Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... | 2014 | 121 | 8.1 | 757074 | 333.13 | 76.0 |
| 1 | 2 | Prometheus | Adventure,Mystery,Sci-Fi | Following clues to the origin of mankind, a te... | Ridley Scott | Noomi Rapace, Logan Marshall-Green, Michael Fa... | 2012 | 124 | 7.0 | 485820 | 126.46 | 65.0 |
| 2 | 3 | Split | Horror,Thriller | Three girls are kidnapped by a man with a diag... | M. Night Shyamalan | James McAvoy, Anya Taylor-Joy, Haley Lu Richar... | 2016 | 117 | 7.3 | 157606 | 138.12 | 62.0 |
| 3 | 4 | Sing | Animation,Comedy,Family | In a city of humanoid animals, a hustling thea... | Christophe Lourdelet | Matthew McConaughey,Reese Witherspoon, Seth Ma... | 2016 | 108 | 7.2 | 60545 | 270.32 | 59.0 |
| 4 | 5 | Suicide Squad | Action,Adventure,Fantasy | A secret government agency recruits some of th... | David Ayer | Will Smith, Jared Leto, Margot Robbie, Viola D... | 2016 | 123 | 6.2 | 393727 | 325.02 | 40.0 |
In [46]:
imdb_rating = imdb[['Rating']]
In [47]:
imdb_rating
Out[47]:
| Rating | |
|---|---|
| 0 | 8.1 |
| 1 | 7.0 |
| 2 | 7.3 |
| 3 | 7.2 |
| 4 | 6.2 |
| ... | ... |
| 995 | 6.2 |
| 996 | 5.5 |
| 997 | 6.2 |
| 998 | 5.6 |
| 999 | 5.3 |
1000 rows × 1 columns
In [38]:
imbd_titles = imdb[['Title','Year','Genre']]
In [39]:
imbd_titles
Out[39]:
| Title | Year | Genre | |
|---|---|---|---|
| 0 | Guardians of the Galaxy | 2014 | Action,Adventure,Sci-Fi |
| 1 | Prometheus | 2012 | Adventure,Mystery,Sci-Fi |
| 2 | Split | 2016 | Horror,Thriller |
| 3 | Sing | 2016 | Animation,Comedy,Family |
| 4 | Suicide Squad | 2016 | Action,Adventure,Fantasy |
| ... | ... | ... | ... |
| 995 | Secret in Their Eyes | 2015 | Crime,Drama,Mystery |
| 996 | Hostel: Part II | 2007 | Horror |
| 997 | Step Up 2: The Streets | 2008 | Drama,Music,Romance |
| 998 | Search Party | 2014 | Adventure,Comedy |
| 999 | Nine Lives | 2016 | Comedy,Family,Fantasy |
1000 rows × 3 columns
In [50]:
ratings = pd.DataFrame({'Title':imbd_titles['Title'],
'Release Year':imbd_titles['Year'],
'Rating':imdb_rating['Rating'],
'Genre':imbd_titles['Genre']})
In [51]:
ratings
Out[51]:
| Title | Release Year | Rating | Genre | |
|---|---|---|---|---|
| 0 | Guardians of the Galaxy | 2014 | 8.1 | Action,Adventure,Sci-Fi |
| 1 | Prometheus | 2012 | 7.0 | Adventure,Mystery,Sci-Fi |
| 2 | Split | 2016 | 7.3 | Horror,Thriller |
| 3 | Sing | 2016 | 7.2 | Animation,Comedy,Family |
| 4 | Suicide Squad | 2016 | 6.2 | Action,Adventure,Fantasy |
| ... | ... | ... | ... | ... |
| 995 | Secret in Their Eyes | 2015 | 6.2 | Crime,Drama,Mystery |
| 996 | Hostel: Part II | 2007 | 5.5 | Horror |
| 997 | Step Up 2: The Streets | 2008 | 6.2 | Drama,Music,Romance |
| 998 | Search Party | 2014 | 5.6 | Adventure,Comedy |
| 999 | Nine Lives | 2016 | 5.3 | Comedy,Family,Fantasy |
1000 rows × 4 columns
In [52]:
ratings.columns
Out[52]:
Index(['Title', 'Release Year', 'Rating', 'Genre'], dtype='object')
In [54]:
ratings.drop_duplicates(subset=['Title','Release Year','Rating'],inplace=True)
In [55]:
ratings
Out[55]:
| Title | Release Year | Rating | Genre | |
|---|---|---|---|---|
| 0 | Guardians of the Galaxy | 2014 | 8.1 | Action,Adventure,Sci-Fi |
| 1 | Prometheus | 2012 | 7.0 | Adventure,Mystery,Sci-Fi |
| 2 | Split | 2016 | 7.3 | Horror,Thriller |
| 3 | Sing | 2016 | 7.2 | Animation,Comedy,Family |
| 4 | Suicide Squad | 2016 | 6.2 | Action,Adventure,Fantasy |
| ... | ... | ... | ... | ... |
| 995 | Secret in Their Eyes | 2015 | 6.2 | Crime,Drama,Mystery |
| 996 | Hostel: Part II | 2007 | 5.5 | Horror |
| 997 | Step Up 2: The Streets | 2008 | 6.2 | Drama,Music,Romance |
| 998 | Search Party | 2014 | 5.6 | Adventure,Comedy |
| 999 | Nine Lives | 2016 | 5.3 | Comedy,Family,Fantasy |
1000 rows × 4 columns
In [56]:
ratings.isnull().sum()
Out[56]:
Title 0 Release Year 0 Rating 0 Genre 0 dtype: int64
In [57]:
ratings.dropna()
Out[57]:
| Title | Release Year | Rating | Genre | |
|---|---|---|---|---|
| 0 | Guardians of the Galaxy | 2014 | 8.1 | Action,Adventure,Sci-Fi |
| 1 | Prometheus | 2012 | 7.0 | Adventure,Mystery,Sci-Fi |
| 2 | Split | 2016 | 7.3 | Horror,Thriller |
| 3 | Sing | 2016 | 7.2 | Animation,Comedy,Family |
| 4 | Suicide Squad | 2016 | 6.2 | Action,Adventure,Fantasy |
| ... | ... | ... | ... | ... |
| 995 | Secret in Their Eyes | 2015 | 6.2 | Crime,Drama,Mystery |
| 996 | Hostel: Part II | 2007 | 5.5 | Horror |
| 997 | Step Up 2: The Streets | 2008 | 6.2 | Drama,Music,Romance |
| 998 | Search Party | 2014 | 5.6 | Adventure,Comedy |
| 999 | Nine Lives | 2016 | 5.3 | Comedy,Family,Fantasy |
1000 rows × 4 columns
In [58]:
joint_data = ratings.merge(netflix,left_on='Title',right_on='title',how='inner')
In [59]:
joint_data
Out[59]:
| Title | Release Year | Rating | Genre | show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Mindhorn | 2016 | 6.4 | Comedy | 80157866 | Movie | Mindhorn | Sean Foley | Julian Barratt, Andrea Riseborough, Essie Davi... | United Kingdom | May 12, 2017 | 2017 | TV-MA | 88 min | Comedies, Cult Movies, Independent Movies | When a twisted killer tells the police he'll o... |
| 1 | Moonlight | 2016 | 7.5 | Drama | 80121348 | Movie | Moonlight | Barry Jenkins | Trevante Rhodes, André Holland, Janelle Monáe,... | United States | May 21, 2019 | 2016 | R | 111 min | Dramas, Independent Movies, LGBTQ Movies | In a crime-infested Miami neighborhood, a gay ... |
| 2 | Fallen | 2016 | 5.6 | Adventure,Drama,Fantasy | 1192866 | Movie | Fallen | Gregory Hoblit | Denzel Washington, John Goodman, Donald Suther... | United States | November 1, 2019 | 1998 | R | 124 min | Thrillers | A tough homicide cop faces his most dangerous ... |
| 3 | The Last Face | 2016 | 3.7 | Drama | 80115030 | Movie | The Last Face | Sean Penn | Javier Bardem, Charlize Theron, Adèle Exarchop... | United States | January 13, 2020 | 2016 | R | 131 min | Dramas | Savage civil war and a dispute over humanitari... |
| 4 | The Autopsy of Jane Doe | 2016 | 6.8 | Horror,Mystery,Thriller | 80022613 | Movie | The Autopsy of Jane Doe | André Øvredal | Emile Hirsch, Brian Cox, Ophelia Lovibond, Mic... | United Kingdom, United States | December 30, 2018 | 2016 | R | 86 min | Horror Movies, Independent Movies, Thrillers | A father-son team of small-town coroners perfo... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | Across the Universe | 2007 | 7.4 | Drama,Fantasy,Musical | 70045863 | Movie | Across the Universe | Julie Taymor | Evan Rachel Wood, Jim Sturgess, Joe Anderson, ... | United States, United Kingdom | January 1, 2019 | 2007 | PG-13 | 133 min | Dramas, Music & Musicals, Romantic Movies | An American girl and a British lad fall in lov... |
| 196 | Taare Zameen Par | 2007 | 8.5 | Drama,Family,Music | 70087087 | Movie | Taare Zameen Par | Aamir Khan | Aamir Khan, Darsheel Safary, Tanay Chheda, Tis... | India | December 8, 2017 | 2007 | PG | 162 min | Dramas, International Movies | When daydreamer Ishaan is sent to boarding sch... |
| 197 | Take Me Home Tonight | 2011 | 6.3 | Comedy,Drama,Romance | 70117577 | Movie | Take Me Home Tonight | Michael Dowse | Topher Grace, Anna Faris, Dan Fogler, Teresa P... | United States, Germany | May 16, 2019 | 2011 | R | 97 min | Comedies, Romantic Movies | Set in the financial boom of the late 1980s, t... |
| 198 | Resident Evil: Afterlife | 2010 | 5.9 | Action,Adventure,Horror | 70128695 | Movie | Resident Evil: Afterlife | Paul W.S. Anderson | Milla Jovovich, Ali Larter, Kim Coates, Shawn ... | Germany, France, United States, Canada, United... | January 1, 2020 | 2010 | R | 97 min | Action & Adventure, Horror Movies, Sci-Fi & Fa... | The Undead Apocalypse continues as super-soldi... |
| 199 | Secret in Their Eyes | 2015 | 6.2 | Crime,Drama,Mystery | 80049281 | Movie | Secret in Their Eyes | Billy Ray | Chiwetel Ejiofor, Nicole Kidman, Julia Roberts... | United States, United Kingdom, Spain, South Korea | April 1, 2018 | 2015 | PG-13 | 111 min | Dramas, Thrillers | A former FBI investigator reopens the haunting... |
200 rows × 16 columns
In [60]:
joint_data = joint_data.sort_values(by='Rating',ascending=False)
In [61]:
joint_data
Out[61]:
| Title | Release Year | Rating | Genre | show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | Dangal | 2016 | 8.8 | Action,Biography,Drama | 80166185 | Movie | Dangal | Nitesh Tiwari | Aamir Khan, Sakshi Tanwar, Fatima Sana Shaikh,... | India | June 21, 2017 | 2016 | TV-PG | 161 min | Dramas, International Movies, Sports Movies | A once-promising wrestler pursues the gold med... |
| 8 | Inception | 2010 | 8.8 | Action,Adventure,Sci-Fi | 70131314 | Movie | Inception | Christopher Nolan | Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen... | United States, United Kingdom | January 1, 2020 | 2010 | PG-13 | 148 min | Action & Adventure, Sci-Fi & Fantasy, Thrillers | In this mind-bending sci-fi thriller, a man ru... |
| 196 | Taare Zameen Par | 2007 | 8.5 | Drama,Family,Music | 70087087 | Movie | Taare Zameen Par | Aamir Khan | Aamir Khan, Darsheel Safary, Tanay Chheda, Tis... | India | December 8, 2017 | 2007 | PG | 162 min | Dramas, International Movies | When daydreamer Ishaan is sent to boarding sch... |
| 83 | The Lives of Others | 2006 | 8.5 | Drama,Thriller | 70056425 | Movie | The Lives of Others | Florian Henckel von Donnersmarck | Ulrich Mühe, Martina Gedeck, Sebastian Koch, U... | Germany | March 15, 2019 | 2006 | R | 138 min | Dramas, International Movies, Thrillers | As a secret police agent eavesdrops on a succe... |
| 11 | The Departed | 2006 | 8.5 | Crime,Drama,Thriller | 70044689 | Movie | The Departed | Martin Scorsese | Leonardo DiCaprio, Matt Damon, Jack Nicholson,... | United States | December 1, 2019 | 2006 | R | 151 min | Dramas, Thrillers | Two rookie Boston cops are sent deep undercove... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 90 | Movie 43 | 2013 | 4.3 | Comedy,Romance | 70222860 | Movie | Movie 43 | Peter Farrelly, Will Graham, Steve Carr, Griff... | Greg Kinnear, Dennis Quaid, Common, Seth MacFa... | United States | April 9, 2019 | 2013 | R | 94 min | Comedies | An eye-popping cast stars in this sketch-comed... |
| 117 | 2307: Winter's Dream | 2016 | 4.0 | Sci-Fi | 80184973 | Movie | 2307: Winter's Dream | Joey Curtis | Paul Sidhu, Arielle Holmes, Branden Coles, Kel... | United States | March 1, 2018 | 2016 | TV-MA | 101 min | Action & Adventure, Independent Movies, Sci-Fi... | In the frozen tundra of a futuristic Arizona w... |
| 69 | The Black Room | 2016 | 3.9 | Horror | 80184868 | Movie | The Black Room | Rolfe Kanefsky | Natasha Henstridge, Lukas Hassel, Lin Shaye, D... | United States | August 7, 2017 | 2016 | TV-MA | 95 min | Horror Movies | A couple's new dream home morphs into a nightm... |
| 3 | The Last Face | 2016 | 3.7 | Drama | 80115030 | Movie | The Last Face | Sean Penn | Javier Bardem, Charlize Theron, Adèle Exarchop... | United States | January 13, 2020 | 2016 | R | 131 min | Dramas | Savage civil war and a dispute over humanitari... |
| 178 | The Intent | 2016 | 3.5 | Crime,Drama | 80178078 | Movie | The Intent | Femi Oyeniran, Kalvadour Peterson | Dylan Duffus, Scorcher, Shone Romulus, Jade As... | United Kingdom | May 15, 2017 | 2016 | TV-MA | 99 min | Dramas, International Movies, Thrillers | After burgeoning criminal Gunz joins the incre... |
200 rows × 16 columns
In [62]:
import plotly.express as px
In [63]:
top_rated = joint_data[:10]
In [64]:
px.sunburst(top_rated,path=['title','country'],values='Rating',color='Rating')
In [65]:
country_count = joint_data['country'].value_counts().sort_values(ascending=False)
In [66]:
country_count
Out[66]:
United States 89
India 9
United Kingdom, United States 8
United Kingdom 8
United States, United Kingdom 5
..
France, United States, Mexico 1
Mexico, Spain 1
United States, Canada, United Kingdom 1
United Kingdom, South Africa 1
South Africa, United States, New Zealand, Canada 1
Name: country, Length: 68, dtype: int64
In [67]:
country_count = pd.DataFrame(country_count)
In [69]:
country_count.head(10)
Out[69]:
| country | |
|---|---|
| United States | 89 |
| India | 9 |
| United Kingdom, United States | 8 |
| United Kingdom | 8 |
| United States, United Kingdom | 5 |
| Australia | 4 |
| Canada | 4 |
| Canada, United States | 3 |
| United States, Canada | 2 |
| United States, China | 2 |
In [76]:
top_countries = country_count[:10]
top_countries.index
Out[76]:
Index(['United States', 'India', 'United Kingdom, United States',
'United Kingdom', 'United States, United Kingdom', 'Australia',
'Canada', 'Canada, United States', 'United States, Canada',
'United States, China'],
dtype='object')
In [74]:
px.funnel(top_countries)
In [77]:
data = dict(number=[89,9,8,8,5,4,4,3,2,2],
country=['United States', 'India', 'United Kingdom, United States','United Kingdom', 'United States, United Kingdom',
'Australia','Canada', 'Canada, United States', 'United States, Canada','United States, China'])
In [78]:
px.funnel(data,x='number',y='country')
In [79]:
netflix_movies
Out[79]:
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 81145628 | Movie | Norm of the North: King Sized Adventure | Richard Finn, Tim Maltby | Alan Marriott, Andrew Toth, Brian Dobson, Cole... | United States, India, South Korea, China | September 9, 2019 | 2019 | TV-PG | 90 min | Children & Family Movies, Comedies | Before planning an awesome wedding for his gra... |
| 1 | 80117401 | Movie | Jandino: Whatever it Takes | NaN | Jandino Asporaat | United Kingdom | September 9, 2016 | 2016 | TV-MA | 94 min | Stand-Up Comedy | Jandino Asporaat riffs on the challenges of ra... |
| 4 | 80125979 | Movie | #realityhigh | Fernando Lebrija | Nesta Cooper, Kate Walsh, John Michael Higgins... | United States | September 8, 2017 | 2017 | TV-14 | 99 min | Comedies | When nerdy high schooler Dani finally attracts... |
| 6 | 70304989 | Movie | Automata | Gabe Ibáñez | Antonio Banderas, Dylan McDermott, Melanie Gri... | Bulgaria, United States, Spain, Canada | September 8, 2017 | 2014 | R | 110 min | International Movies, Sci-Fi & Fantasy, Thrillers | In a dystopian future, an insurance adjuster f... |
| 7 | 80164077 | Movie | Fabrizio Copano: Solo pienso en mi | Rodrigo Toro, Francisco Schultz | Fabrizio Copano | Chile | September 8, 2017 | 2017 | TV-MA | 60 min | Stand-Up Comedy | Fabrizio Copano takes audience participation t... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5577 | 80085438 | Movie | Frank and Cindy | G.J. Echternkamp | NaN | United States | April 1, 2016 | 2007 | TV-MA | 70 min | Documentaries | Frank was a rising pop star when he married Ci... |
| 5578 | 80085439 | Movie | Frank and Cindy | G.J. Echternkamp | Rene Russo, Oliver Platt, Johnny Simmons, Jane... | United States | April 1, 2016 | 2015 | R | 102 min | Comedies, Dramas, Independent Movies | A student filmmaker vengefully turns his camer... |
| 5579 | 80011846 | Movie | Iverson | Zatella Beatty | Allen Iverson | United States | April 1, 2016 | 2014 | NR | 88 min | Documentaries, Sports Movies | This unfiltered documentary follows the rocky ... |
| 5580 | 80064521 | Movie | Jeremy Scott: The People's Designer | Vlad Yudin | Jeremy Scott | United States | April 1, 2016 | 2015 | PG-13 | 109 min | Documentaries | The journey of fashion designer Jeremy Scott f... |
| 6231 | 80116008 | Movie | Little Baby Bum: Nursery Rhyme Friends | NaN | NaN | NaN | NaN | 2016 | NaN | 60 min | Movies | Nursery rhymes and original music for children... |
4265 rows × 12 columns
In [80]:
plt.figure(figsize=(12,10))
sns.countplot(y='release_year',data=netflix_movies,palette='coolwarm')
Out[80]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fbef6d688>
In [81]:
plt.figure(figsize=(12,10))
sns.countplot(y='release_year',data=netflix_movies,palette='coolwarm',order=netflix_movies['release_year'].value_counts().index[:15])
Out[81]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fbf18a808>
In [83]:
netflix_movies['country'].isnull().sum()
Out[83]:
195
In [84]:
netflix_movies.isnull().sum()
Out[84]:
show_id 0 type 0 title 0 director 128 cast 360 country 195 date_added 1 release_year 0 rating 8 duration 0 listed_in 0 description 0 dtype: int64
In [85]:
countries={}
In [86]:
netflix_movies['country'] = netflix_movies['country'].fillna('Unknown')
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [89]:
netflix_movies[netflix_movies['country']=='Unknown']
Out[89]:
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | 80169755 | Movie | JoaquÃn Reyes: Una y no más | José Miguel Contreras | JoaquÃn Reyes | Unknown | September 8, 2017 | 2017 | TV-MA | 78 min | Stand-Up Comedy | Comedian and celebrity impersonator JoaquÃn Re... |
| 12 | 80182480 | Movie | Krish Trish and Baltiboy | NaN | Damandeep Singh Baggan, Smita Malhotra, Baba S... | Unknown | September 8, 2017 | 2009 | TV-Y7 | 58 min | Children & Family Movies | A team of minstrels, including a monkey, cat a... |
| 13 | 80182483 | Movie | Krish Trish and Baltiboy: Battle of Wits | Munjal Shroff, Tilak Shetty | Damandeep Singh Baggan, Smita Malhotra, Baba S... | Unknown | September 8, 2017 | 2013 | TV-Y7 | 62 min | Children & Family Movies | An artisan is cheated of his payment, a lion o... |
| 14 | 80182596 | Movie | Krish Trish and Baltiboy: Best Friends Forever | Munjal Shroff, Tilak Shetty | Damandeep Singh Baggan, Smita Malhotra, Deepak... | Unknown | September 8, 2017 | 2016 | TV-Y | 65 min | Children & Family Movies | A cat, monkey and donkey team up to narrate fo... |
| 15 | 80182482 | Movie | Krish Trish and Baltiboy: Comics of India | Tilak Shetty | Damandeep Singh Baggan, Smita Malhotra, Baba S... | Unknown | September 8, 2017 | 2012 | TV-Y7 | 61 min | Children & Family Movies | In three comic-strip-style tales, a boy tries ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5374 | 81035850 | Movie | My Wife and My Wifey | Moataz El Tony | Ramez Galal, Shery Adel, Hassan Hosny, Samy Ma... | Unknown | April 18, 2019 | 2014 | TV-14 | 99 min | Comedies, International Movies | A man finds his marriage to a dedicated women'... |
| 5385 | 81013626 | Movie | HOMECOMING: A film by Beyoncé | Beyoncé Knowles-Carter | Beyoncé Knowles-Carter | Unknown | April 17, 2019 | 2019 | TV-MA | 138 min | Documentaries, Music & Musicals | This intimate, in-depth look at Beyoncé's cele... |
| 5394 | 80999069 | Movie | Super Monsters Furever Friends | NaN | Elyse Maloway, Vincent Tong, Erin Mathews, And... | Unknown | April 16, 2019 | 2019 | TV-Y | 59 min | Children & Family Movies | On the first night of spring, the Super Monste... |
| 5522 | 80196139 | Movie | Fishpeople | Keith Malloy | NaN | Unknown | April 1, 2018 | 2017 | TV-14 | 49 min | Documentaries, Sports Movies | In this exploration of the life-changing power... |
| 6231 | 80116008 | Movie | Little Baby Bum: Nursery Rhyme Friends | NaN | NaN | Unknown | NaN | 2016 | NaN | 60 min | Movies | Nursery rhymes and original music for children... |
195 rows × 12 columns
In [91]:
cou = list(netflix_movies['country'])
In [92]:
cou
Out[92]:
['United States, India, South Korea, China', 'United Kingdom', 'United States', 'Bulgaria, United States, Spain, Canada', 'Chile', 'United States, United Kingdom, Denmark, Sweden', 'Unknown', 'Netherlands, Belgium, United Kingdom, United States', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'France, Belgium', 'United States', 'France, Belgium', 'United States, Uruguay', 'United States', 'United States', 'United States', 'United States,', 'Thailand', 'China, Canada, United States', 'United States', 'Belgium, United Kingdom, United States', 'Belgium, France', 'India', 'Unknown', 'India', 'Unknown', 'United States', 'India', 'United Kingdom', 'United Kingdom', 'Unknown', 'United States, Canada', 'Thailand', 'Thailand', 'Thailand', 'Thailand', 'Thailand', 'Thailand', 'United States', 'United States', 'Pakistan', 'Canada', 'United States', 'India', 'United States', 'United Kingdom, France', 'United Kingdom', 'United States, United Kingdom', 'South Korea', 'Denmark, United States', 'United Kingdom, United States', 'Turkey, United States', 'Brazil', 'United States', 'United States', 'United States', 'Unknown', 'United States', 'Unknown', 'Denmark, France, Italy, Belgium, Netherlands', 'Unknown', 'Indonesia', 'China', 'United States', 'United States', 'Indonesia', 'Indonesia', 'Indonesia', 'Spain', 'Ireland, United Kingdom', 'Turkey', 'United States', 'United States', 'Hong Kong', 'France, Morocco', 'United States', 'India', 'Hong Kong, China', 'United States', 'United States', 'United States', 'United States', 'United States, Mexico', 'Vietnam', 'France, Canada', 'India', 'India', 'United States', 'United States', 'United States', 'United Kingdom', 'United States', 'United States', 'China', 'Canada', 'Spain, Argentina', 'India', 'United States', 'United States', 'United States', 'India', 'United States', 'United Kingdom, United States', 'United States', 'United States', 'United States', 'Nigeria', 'Nigeria', 'France', 'Nigeria', 'United States', 'Brazil', 'United States', 'United States', 'Turkey', 'Canada', 'Hong Kong', 'Hong Kong', 'United States, Greece, United Kingdom', 'United States', 'Vietnam', 'China', 'United States', 'Norway, United Kingdom, France, Ireland', 'India', 'France, Switzerland, Spain, United States, United Arab Emirates', 'United States', 'United States', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Canada, United States, United Kingdom', 'United States', 'United States', 'United States', 'United States', 'United Kingdom', 'United States', 'Hong Kong', 'United States', 'United Kingdom, Canada, United States', 'United States', 'United States', 'United States, United Kingdom, Canada, Japan', 'Canada, United States', 'United States', 'United States', 'United States', 'United Kingdom', 'United States', 'United States', 'United States', 'Indonesia', 'Ireland', 'India', 'Indonesia', 'Indonesia', 'India', 'Unknown', 'India', 'India', 'India', 'India', 'Unknown', 'Cambodia, United States', 'Russia', 'Pakistan', 'United States, Mexico', 'Mexico', 'United States, Denmark', 'United States', 'United States', 'United States', 'India', 'United States', 'United States', 'Thailand', 'Japan', 'Thailand', 'Israel, United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Turkey', 'China', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Italy', 'United States', 'United States', 'Netherlands', 'United Kingdom', 'United States, United Kingdom', 'United States', 'Unknown', 'United Kingdom', 'United States', 'United States', 'United States, Canada', 'United States', 'Brazil, United States', 'United Kingdom, Canada, United States', 'United States', 'United States, France', 'Germany, United States, Canada', 'Nigeria', 'United States', 'United States', 'United States, Australia', 'Nigeria', 'Denmark, Brazil, France, Portugal, Sweden', 'United States', 'Nigeria', 'United States', 'United States', 'United States', 'United States', 'United States', 'Nigeria', 'Nigeria', 'United States', 'France, United States', 'United States', 'United States, United Kingdom', 'United Arab Emirates', 'Egypt', 'United States', 'Spain', 'United States', 'France, Belgium', 'United States', 'India', 'India', 'India', 'India', 'United States', 'India, Germany, Austria', 'Thailand', 'Mexico', 'France', 'United States', 'United States', 'United States', 'Unknown', 'Thailand', 'United States', 'Unknown', 'India', 'Unknown', 'Japan', 'Japan', 'United States', 'India', 'Czech Republic, France', 'India', 'United States, Senegal', 'Japan', 'Japan', 'Japan', 'India', 'United States', 'India', 'India', 'India', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'Germany', 'United States', 'France', 'United States', 'India, Soviet Union', 'Argentina, Spain', 'United States, Hong Kong', 'China', 'United Kingdom, Italy, Israel, Peru, United States', 'Argentina, Uruguay, Spain, France', 'Argentina', 'United States', 'Netherlands', 'Netherlands', 'Pakistan, Norway, United States', 'United Kingdom', 'Canada, United States', 'United States', 'Spain', 'United Kingdom', 'Spain', 'United States', 'United States', 'United States', 'Indonesia', 'United States', 'Indonesia', 'United Kingdom, Ukraine, United States', 'United States', 'United States', 'Malaysia', 'Australia, United States', 'United States', 'United States', 'United States', 'Ireland, South Africa', 'Indonesia', 'Vietnam', 'United States', 'China, India, Nepal', 'United States', 'India', 'Indonesia', 'Indonesia', 'United Kingdom', 'United States', 'Unknown', 'United Kingdom, Hong Kong', 'Turkey', 'Canada', 'Turkey', 'United States', 'United Kingdom, Canada, Italy', 'Indonesia', 'United States', 'United States', 'Argentina', 'Spain, France', 'Philippines', 'United States', 'United States', 'United States', 'India', 'India', 'India', 'India', 'Indonesia', 'United States', 'United States', 'United States', 'United Kingdom, India, United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'France, Belgium, China, United States', 'Canada', 'Mexico, United States', 'United Kingdom', 'United States, Indonesia', 'Canada', 'Canada, United States', 'United Kingdom, Germany, Canada', 'France', 'France, Belgium', 'France', 'United States', 'Hong Kong, China', 'United States', 'Spain', 'United States', 'United States', 'Unknown', 'Russia, Poland, Serbia', 'Serbia, United States', 'Spain, Portugal', 'Colombia', 'United States, United Kingdom', 'India', 'United States', 'Israel, United States', 'United States', 'Turkey', 'United States', 'United States, United Kingdom, Germany', 'Unknown', 'United States', 'Switzerland', 'United States', 'United Kingdom', 'United States', 'United States', 'United States', 'Singapore, Malaysia', 'India', 'United Kingdom', 'United States', 'Canada, Luxembourg', 'United States', 'Hong Kong, China', 'United States', 'United States', 'United States', 'United States, Spain, Germany', 'United States', 'United States', 'United States', 'United States', 'Egypt, Austria, United States', 'United States', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'United States', 'United Kingdom', 'Indonesia', 'United States', 'United States', 'Peru', 'United Kingdom, United States, Morocco', 'United States', 'United States', 'United States', 'United States', 'Mexico', 'United States', 'United States, Bulgaria', 'United States', 'Unknown', 'Unknown', 'United Kingdom', 'United Kingdom', 'United States', 'United Kingdom, United States', 'United Kingdom', 'United Kingdom', 'United Kingdom', 'United States', 'United States', 'United States', 'Unknown', 'United States', 'Nigeria', 'Belgium, Luxembourg, France', 'Mexico, Argentina', 'Unknown', 'United Kingdom, Canada, United States, Cayman Islands', 'South Korea', 'India', 'Unknown', 'Hong Kong, China', 'United States, United Kingdom, Morocco', 'Unknown', 'Unknown', 'Indonesia, United States', 'France', 'South Africa', 'Nigeria', 'United States', 'Unknown', 'Turkey', 'Nigeria', 'Spain', 'Nigeria', 'United Kingdom', 'United States', 'Nigeria', 'India', 'Indonesia', 'India', 'Unknown', 'United States, China', 'Canada', 'United States', 'United States', 'United States', 'South Korea', 'United States, United Kingdom', 'United States', 'United States', 'India', 'United Kingdom, United States', 'India', 'Netherlands, Denmark, South Africa', 'United States', 'United States', 'United States', 'United Kingdom, Poland, United States', 'Unknown', 'India', 'India', 'India', 'India', 'India', 'New Zealand', 'India', 'India', 'Venezuela', 'India', 'India', 'India', 'United States, Spain', 'India', 'Australia, United Arab Emirates', 'Australia, India', 'India, Malaysia', 'India', 'India', 'India', 'India', 'India', 'Canada, India, Thailand, United States, United Arab Emirates', 'France', 'Japan', 'Japan', 'Japan', 'France', 'Japan', 'Japan', 'Canada', 'China', 'United States', 'Italy, France', 'United States', 'France', 'India', 'Germany, Jordan, Netherlands', 'Turkey, France, Germany, Poland', 'Mexico', 'United Kingdom', 'United States', 'United States', 'United States', 'United Kingdom, United States', 'Spain, France', 'United States', 'Indonesia', 'United States', 'United States, Israel, United Kingdom, Canada', 'Saudi Arabia', 'Spain', 'United States', 'United States', 'Turkey', 'United States', 'United States', 'Nigeria', 'Japan', 'Spain, France', 'Indonesia', 'Egypt, France', 'Unknown', 'Norway, Iceland, United States', 'United States', 'United States', 'United States', 'Denmark, France, Poland', 'Canada', 'United States, Germany, Canada', 'United States', 'United States', 'United States', 'United States', 'United States, United Kingdom', 'United States, Germany', 'United States', 'United States, Germany', 'United States', 'United States', 'Poland', 'United States', 'Netherlands', 'United States', 'United States, Canada', 'United States', 'United States', 'Poland,', 'United States', 'Poland', 'United States, United Kingdom, Australia', 'India', 'Unknown', 'Poland, West Germany', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Germany, United States', 'United States', 'United Kingdom, France, United States', 'United States', 'Peru', 'United Kingdom, United States', 'United States', 'United States', 'United States', 'United States', 'Poland', 'Nigeria', 'United States', 'United States', 'Poland', 'United States', 'United States', 'Poland', 'United States', 'United States', 'United Kingdom', 'United States, Malta, United Kingdom', 'United States', 'United States', 'Poland', 'Poland', 'India', 'Canada', 'India', 'India', 'India', 'United States', 'United States, Sweden', 'India', 'India', 'India', 'Unknown', 'India', 'United States', 'Unknown', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'United States', 'United States', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'Australia', 'India', 'India', 'India', 'United States', 'United States, Canada', 'United States', 'India', 'India', 'India', 'India', 'India, United States', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'Unknown', 'India', 'India', 'India', 'United States, United Kingdom, Germany', 'India', 'India', 'India, Australia', 'India', 'United States', 'Pakistan', 'South Korea', 'France, Canada, Belgium', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States, Canada', 'United States, Canada', 'United States', 'United States', 'Brazil', 'Spain', 'United States, Italy', 'United States, Brazil', 'United States', 'Canada', 'United Kingdom', 'United States', 'Canada, Ireland, United States', 'United States, France, Canada, Lebanon, Qatar', 'Japan', 'United States', 'United States', 'Switzerland, France', 'France, Belgium', 'New Zealand', 'Norway, Germany', 'South Korea', 'United Kingdom, Canada, Japan', 'Mexico', 'United States', 'India', 'Unknown', 'Brazil', 'India', 'Chile, United States, France', 'Spain', 'United States, Canada', 'Unknown', 'Netherlands', 'India', 'United States', 'Italy', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Hong Kong', 'Brazil, France', 'Germany', 'United Kingdom, United States', 'United States', 'United States', 'France', 'United States', 'Argentina, Chile', 'United States', 'Thailand', 'Thailand', 'Italy', 'India', 'Canada', 'United States', 'United States', 'United States', 'United Kingdom,', 'United States', 'China, Hong Kong', 'United States', 'Unknown', 'United States', 'United States', 'South Korea', 'United States', 'India', 'United States', 'India', 'United States', 'Canada, United States', 'Canada', 'South Africa, United States, New Zealand, Canada', 'United States', 'United States', 'India', 'United Kingdom', 'United States', 'Austria', 'Unknown', 'India', 'United States', 'Ireland, Canada', 'Mexico', 'Indonesia', 'Italy, Switzerland, France, Germany', 'Indonesia', 'India', 'India', 'France', 'Mexico, Netherlands', 'Peru, United States, United Kingdom', 'United States', 'United States', 'France, Senegal, Belgium', 'Nigeria', 'France', 'South Africa', 'Unknown', 'Nigeria', 'Nigeria', 'Nigeria', 'United States', 'Germany, Canada, United States', 'China', 'United States', 'India', 'Unknown', 'Canada, Norway', 'United States', 'China, Morocco, Hong Kong', 'India', 'United States', 'United States', 'Unknown', 'India', 'Unknown', 'United States', 'United States', 'Uruguay', 'Mexico', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Spain', 'United States', 'United States', 'United States', 'United States, United Arab Emirates', 'Spain, Belgium, Switzerland, United States, China, United Kingdom', 'France', 'Japan', 'United States', 'United States', 'United States', 'United States', 'India', 'United States', 'India', 'United Kingdom, Germany, Canada, United States', 'United States', 'United States', 'Spain', 'Australia, Canada', 'United States', 'United States', 'United States', 'Mexico', 'Australia, France', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'India', 'United States', 'United States', 'United States', 'United States', 'United States', 'Mexico', 'Germany, United Kingdom', 'India', 'Italy, United States', 'United States', 'United States', 'United States, New Zealand, United Kingdom', 'United States', 'United States', 'United States', 'United States', 'United States', 'United Kingdom, Germany, United States', 'United States, Germany', 'United States', 'United States', 'United States', 'United States', 'United States', 'United States', 'Indonesia', 'Indonesia', 'United States', 'United States', 'United States', 'United States, Canada', 'United States, Australia, Mexico', 'United States', 'United States, South Korea, Japan', 'United States, Canada', 'United States', 'United States', 'United States', 'United States, Canada', 'United States', 'Brazil', 'United States', 'United States', 'France, Iran, United States', 'United States', 'United Kingdom', 'United Kingdom, Australia, Canada, United States', 'United States', 'United States', 'United States', 'South Korea', 'United States', 'France, Qatar', 'United Kingdom', 'United States', 'United States', 'United States', 'Canada, United States', 'India', 'United States', 'United States', 'United States', 'Finland, Germany, Belgium', 'United States', 'United Kingdom, France', 'United States, Spain, Chile, Peru', 'United States', 'United Arab Emirates, United States, United Kingdom', 'France, Belgium', 'United States', 'United States', 'United States', 'United States', 'Colombia', 'Argentina', 'India', 'India', 'Japan', 'Spain', 'United States, Ireland', 'India, United States', 'United States', 'Japan', 'India', 'Canada', 'India', 'India', 'Indonesia', 'India', 'India', 'Australia, Iraq', 'India', 'India', 'United States', 'Australia', 'Australia', 'France', 'Spain', 'India', 'India', 'Germany', 'India', 'United States', 'France', 'United States', 'United States', 'United States', 'United States, Mexico', 'Philippines', 'Unknown', 'United States', 'United States', 'United States', 'United States', 'Canada', 'United States', 'United States', 'United States', 'Canada', 'United States', 'France', 'United States', 'Mexico', 'United States', 'Brazil', 'United States', 'United States, Germany', 'United States', 'Spain', 'United States', 'United States', 'United States', ...]
In [93]:
for i in cou:
i = list(i.split(','))
if len(i)==1:
if i in list(countries.keys()):
countries[i]+=1
else:
countries[i[0]]=1
else:
for j in i:
if j in list(countries.keys()):
countries[j]+1
else:
countries[j]=1
In [95]:
countries
Out[95]:
{'United States': 1,
' India': 1,
' South Korea': 1,
' China': 1,
'United Kingdom': 1,
'Bulgaria': 1,
' United States': 1,
' Spain': 1,
' Canada': 1,
'Chile': 1,
' United Kingdom': 1,
' Denmark': 1,
' Sweden': 1,
'Unknown': 1,
'Netherlands': 1,
' Belgium': 1,
'France': 1,
' Uruguay': 1,
'': 1,
'Thailand': 1,
'China': 1,
'Belgium': 1,
' France': 1,
'India': 1,
'Pakistan': 1,
'Canada': 1,
'South Korea': 1,
'Denmark': 1,
'Turkey': 1,
'Brazil': 1,
' Italy': 1,
' Netherlands': 1,
'Indonesia': 1,
'Spain': 1,
'Ireland': 1,
'Hong Kong': 1,
' Morocco': 1,
' Mexico': 1,
'Vietnam': 1,
' Argentina': 1,
'Nigeria': 1,
' Greece': 1,
'Norway': 1,
' Ireland': 1,
' Switzerland': 1,
' United Arab Emirates': 1,
' Japan': 1,
'Cambodia': 1,
'Russia': 1,
'Mexico': 1,
'Japan': 1,
'Israel': 1,
'Italy': 1,
'Germany': 1,
' Australia': 1,
' Brazil': 1,
' Portugal': 1,
'United Arab Emirates': 1,
'Egypt': 1,
' Germany': 1,
' Austria': 1,
'Czech Republic': 1,
' Senegal': 1,
' Soviet Union': 1,
'Argentina': 1,
' Hong Kong': 1,
' Israel': 1,
' Peru': 1,
' Norway': 1,
' Ukraine': 1,
'Malaysia': 1,
'Australia': 1,
' South Africa': 1,
' Nepal': 1,
'Philippines': 1,
' Indonesia': 1,
' Poland': 1,
' Serbia': 1,
'Serbia': 1,
'Colombia': 1,
'Switzerland': 1,
'Singapore': 1,
' Malaysia': 1,
' Luxembourg': 1,
'Peru': 1,
' Bulgaria': 1,
' Cayman Islands': 1,
'South Africa': 1,
'New Zealand': 1,
'Venezuela': 1,
' Thailand': 1,
' Jordan': 1,
'Saudi Arabia': 1,
' Iceland': 1,
'Poland': 1,
' West Germany': 1,
' Malta': 1,
' Lebanon': 1,
' Qatar': 1,
' Chile': 1,
' New Zealand': 1,
'Austria': 1,
'Uruguay': 1,
' Iran': 1,
'Finland': 1,
' Iraq': 1,
' Liechtenstein': 1,
'Taiwan': 1,
' Albania': 1,
' Russia': 1,
' Pakistan': 1,
' Slovakia': 1,
' Czech Republic': 1,
' Samoa': 1,
'Ghana': 1,
' Cambodia': 1,
' Finland': 1,
'Iceland': 1,
' Colombia': 1,
' Botswana': 1,
'Iran': 1,
' Taiwan': 1,
'Sweden': 1,
'Hungary': 1,
'Guatemala': 1,
'Portugal': 1,
' Malawi': 1,
'Paraguay': 1,
'Somalia': 1,
' Kenya': 1,
' Sudan': 1,
' Sri Lanka': 1,
'Dominican Republic': 1,
' Panama': 1,
'Romania': 1,
' Latvia': 1,
' Singapore': 1,
' Uganda': 1,
'Slovenia': 1,
' Croatia': 1,
'Croatia': 1,
' Slovenia': 1,
' Montenegro': 1,
'Bangladesh': 1,
' Vatican City': 1,
' Egypt': 1,
'Soviet Union': 1,
'Lebanon': 1,
' Dominican Republic': 1,
' East Germany': 1,
' Bangladesh': 1,
' Afghanistan': 1,
' Venezuela': 1,
'Georgia': 1,
' Namibia': 1,
' Zimbabwe': 1,
'West Germany': 1,
' Hungary': 1,
' Nicaragua': 1,
' Romania': 1,
' Kazakhstan': 1,
' Turkey': 1,
' Armenia': 1,
' Mongolia': 1,
' Philippines': 1,
' Bermuda': 1,
' Ecuador': 1}
In [96]:
countries_fin={}
In [97]:
for country,no in countries.items():
country = country.replace('','')
if country in list(countries_fin.keys()):
countries_fin[country]+=no
else:
countries_fin[country]=no
countries_fin = {k:v for k, v in sorted(countries_fin.items(),key=lambda item: item[1],reverse=True)}
In [100]:
countries_fin.keys()
Out[100]:
dict_keys(['United States', ' India', ' South Korea', ' China', 'United Kingdom', 'Bulgaria', ' United States', ' Spain', ' Canada', 'Chile', ' United Kingdom', ' Denmark', ' Sweden', 'Unknown', 'Netherlands', ' Belgium', 'France', ' Uruguay', '', 'Thailand', 'China', 'Belgium', ' France', 'India', 'Pakistan', 'Canada', 'South Korea', 'Denmark', 'Turkey', 'Brazil', ' Italy', ' Netherlands', 'Indonesia', 'Spain', 'Ireland', 'Hong Kong', ' Morocco', ' Mexico', 'Vietnam', ' Argentina', 'Nigeria', ' Greece', 'Norway', ' Ireland', ' Switzerland', ' United Arab Emirates', ' Japan', 'Cambodia', 'Russia', 'Mexico', 'Japan', 'Israel', 'Italy', 'Germany', ' Australia', ' Brazil', ' Portugal', 'United Arab Emirates', 'Egypt', ' Germany', ' Austria', 'Czech Republic', ' Senegal', ' Soviet Union', 'Argentina', ' Hong Kong', ' Israel', ' Peru', ' Norway', ' Ukraine', 'Malaysia', 'Australia', ' South Africa', ' Nepal', 'Philippines', ' Indonesia', ' Poland', ' Serbia', 'Serbia', 'Colombia', 'Switzerland', 'Singapore', ' Malaysia', ' Luxembourg', 'Peru', ' Bulgaria', ' Cayman Islands', 'South Africa', 'New Zealand', 'Venezuela', ' Thailand', ' Jordan', 'Saudi Arabia', ' Iceland', 'Poland', ' West Germany', ' Malta', ' Lebanon', ' Qatar', ' Chile', ' New Zealand', 'Austria', 'Uruguay', ' Iran', 'Finland', ' Iraq', ' Liechtenstein', 'Taiwan', ' Albania', ' Russia', ' Pakistan', ' Slovakia', ' Czech Republic', ' Samoa', 'Ghana', ' Cambodia', ' Finland', 'Iceland', ' Colombia', ' Botswana', 'Iran', ' Taiwan', 'Sweden', 'Hungary', 'Guatemala', 'Portugal', ' Malawi', 'Paraguay', 'Somalia', ' Kenya', ' Sudan', ' Sri Lanka', 'Dominican Republic', ' Panama', 'Romania', ' Latvia', ' Singapore', ' Uganda', 'Slovenia', ' Croatia', 'Croatia', ' Slovenia', ' Montenegro', 'Bangladesh', ' Vatican City', ' Egypt', 'Soviet Union', 'Lebanon', ' Dominican Republic', ' East Germany', ' Bangladesh', ' Afghanistan', ' Venezuela', 'Georgia', ' Namibia', ' Zimbabwe', 'West Germany', ' Hungary', ' Nicaragua', ' Romania', ' Kazakhstan', ' Turkey', ' Armenia', ' Mongolia', ' Philippines', ' Bermuda', ' Ecuador'])
In [103]:
plt.figure(figsize=(12,7))
ax = sns.barplot(x=list(countries_fin.keys())[:10],y=list(countries_fin.values())[:10])
ax.set_xticklabels(list(countries_fin.keys())[0:10],rotation = 90)
Out[103]:
[Text(0, 0, 'United States'), Text(0, 0, ' India'), Text(0, 0, ' South Korea'), Text(0, 0, ' China'), Text(0, 0, 'United Kingdom'), Text(0, 0, 'Bulgaria'), Text(0, 0, ' United States'), Text(0, 0, ' Spain'), Text(0, 0, ' Canada'), Text(0, 0, 'Chile')]
In [105]:
netflix_movies['duration'].value_counts()
Out[105]:
90 min 111
91 min 104
92 min 101
94 min 94
95 min 94
...
191 min 1
20 min 1
209 min 1
228 min 1
18 min 1
Name: duration, Length: 186, dtype: int64
In [106]:
netflix_movies['duration'] = netflix_movies['duration'].str.replace('min','')
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [108]:
netflix_movies['duration'][0]
Out[108]:
'90 '
In [109]:
netflix_movies['duration'] = netflix_movies['duration'].astype(str).astype(int)
D:\ram\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [111]:
netflix_movies['duration'][0]
Out[111]:
90
In [112]:
sns.kdeplot(netflix_movies['duration'],shade=True)
Out[112]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fc0320748>
In [113]:
from collections import Counter
In [114]:
genres = list(netflix_movies['listed_in'])
gen = []
for i in genres:
i = list(i.split(','))
for j in i:
gen.append(j.replace('',""))
g = Counter(gen)
In [115]:
g
Out[115]:
Counter({'Children & Family Movies': 358,
' Comedies': 310,
'Stand-Up Comedy': 273,
'Comedies': 803,
'International Movies': 85,
' Sci-Fi & Fantasy': 183,
' Thrillers': 352,
'Action & Adventure': 597,
' Dramas': 546,
' International Movies': 1842,
'Cult Movies': 10,
' Independent Movies': 534,
' Romantic Movies': 374,
'Documentaries': 644,
'Horror Movies': 205,
'Dramas': 1077,
' Music & Musicals': 231,
'Anime Features': 12,
' Faith & Spirituality': 47,
' Horror Movies': 57,
'Independent Movies': 18,
' LGBTQ Movies': 60,
' Cult Movies': 45,
'Movies': 56,
'Thrillers': 40,
'Classic Movies': 62,
' Sports Movies': 156,
' Anime Features': 33,
' Stand-Up Comedy': 8,
' Documentaries': 24,
'Music & Musicals': 12,
'Sci-Fi & Fantasy': 10,
' Children & Family Movies': 20,
' Classic Movies': 22,
'Sports Movies': 1,
'Romantic Movies': 2})
In [116]:
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
In [117]:
text = list(set(gen))
In [118]:
text
Out[118]:
[' Faith & Spirituality', 'Sci-Fi & Fantasy', 'Music & Musicals', ' Cult Movies', 'Independent Movies', ' Stand-Up Comedy', ' International Movies', ' Dramas', 'Sports Movies', 'International Movies', ' Independent Movies', 'Thrillers', ' Music & Musicals', 'Romantic Movies', ' Horror Movies', 'Dramas', ' Classic Movies', 'Comedies', ' Children & Family Movies', ' Sports Movies', ' Comedies', ' Sci-Fi & Fantasy', 'Cult Movies', 'Movies', ' LGBTQ Movies', 'Stand-Up Comedy', 'Action & Adventure', 'Classic Movies', ' Romantic Movies', ' Documentaries', 'Anime Features', ' Anime Features', 'Documentaries', 'Children & Family Movies', 'Horror Movies', ' Thrillers']
In [121]:
plt.figure(figsize=(20,20))
sns.set_style('white')
wordcloud = WordCloud(max_font_size=50,max_words=100,background_color='white').generate(str(text))
plt.imshow(wordcloud)
Out[121]:
<matplotlib.image.AxesImage at 0x20fbe686b48>
In [120]:
plt.figure(figsize=(20,20))
wordcloud = WordCloud(max_font_size=50,max_words=100,background_color='white').generate(str(text))
plt.imshow(wordcloud,interpolation='bilinear')
Out[120]:
<matplotlib.image.AxesImage at 0x20fbe686d08>
In [123]:
g.keys()
Out[123]:
dict_keys(['Children & Family Movies', ' Comedies', 'Stand-Up Comedy', 'Comedies', 'International Movies', ' Sci-Fi & Fantasy', ' Thrillers', 'Action & Adventure', ' Dramas', ' International Movies', 'Cult Movies', ' Independent Movies', ' Romantic Movies', 'Documentaries', 'Horror Movies', 'Dramas', ' Music & Musicals', 'Anime Features', ' Faith & Spirituality', ' Horror Movies', 'Independent Movies', ' LGBTQ Movies', ' Cult Movies', 'Movies', 'Thrillers', 'Classic Movies', ' Sports Movies', ' Anime Features', ' Stand-Up Comedy', ' Documentaries', 'Music & Musicals', 'Sci-Fi & Fantasy', ' Children & Family Movies', ' Classic Movies', 'Sports Movies', 'Romantic Movies'])
In [124]:
g.values()
Out[124]:
dict_values([358, 310, 273, 803, 85, 183, 352, 597, 546, 1842, 10, 534, 374, 644, 205, 1077, 231, 12, 47, 57, 18, 60, 45, 56, 40, 62, 156, 33, 8, 24, 12, 10, 20, 22, 1, 2])
In [126]:
list(g.values())
Out[126]:
[358, 310, 273, 803, 85, 183, 352, 597, 546, 1842, 10, 534, 374, 644, 205, 1077, 231, 12, 47, 57, 18, 60, 45, 56, 40, 62, 156, 33, 8, 24, 12, 10, 20, 22, 1, 2]
In [129]:
x = list(g.keys())
y = list(g.values())
In [131]:
plt.figure(figsize=(30,30))
plt.vlines(x,ymax=0,ymin=y)
Out[131]:
<matplotlib.collections.LineCollection at 0x20fbfe18648>
In [132]:
plt.figure(figsize=(20,20))
sns.barplot(y=list(countries_fin.keys()),x=list(countries_fin.values()))
Out[132]:
<matplotlib.axes._subplots.AxesSubplot at 0x20fc02a44c8>
In [134]:
netflix['description'].describe()
Out[134]:
count 6234 unique 6226 top A surly septuagenarian gets another chance at ... freq 3 Name: description, dtype: object
In [ ]: